Apparatus, System, and Method for Mobile, Low-Cost Headset for 3D Point of Gaze Estimation

ABSTRACT

An apparatus, system, and method for a mobile, low-cost headset for 3D point of gaze estimation. A point of gaze apparatus may include an eye tracking camera configured to track the movements of a user&#39;s eye and a scene camera configured to create a three-dimensional image and a two-dimensional image in the direction of the user&#39;s gaze. The point of gaze apparatus may include an image processing module configured to identify a point of gaze of the user and identify an object located at the user&#39;s point of gaze by using information from the eye tracking camera and the scene camera.

RELATED APPLICATION

This application claims priority to U.S. Provisional Patent Application61/876,038 entitled “Apparatuses, System, and Method for Mobile,Low-Cost Headset for 3D Point of Gaze Estimation,” and filed on Sep. 10,2013, the entire contents of which are incorporated herein by referencewithout disclaimer.

STATEMENT REGARDING FEDERALLY-SPONSORED RESEARCH

This invention was made with government support under grant numbers CNS0923494, CNS 1035913, and IIS 1238660 awarded by the National ScienceFoundation. The government has certain rights in the invention.

BACKGROUND OF THE INVENTION

1. Field of Invention

This invention relates to 3D point of gaze apparatus and moreparticularly relates to an apparatus system and method for mobile,low-cost, head-mounted, 3D point of gaze estimation.

2. Description of Related Art

Eye gaze based interaction has many useful applications in human-machineinterfaces, assistive technologies, and multimodal systems. Traditionalinput methods, such as the keyboard and mouse, are not practical in manysituations and can be ineffective for some users with physicalimpairments. Knowledge of a user's point of gaze (PoG) can be a powerfuldata modality in intelligent systems by facilitating intuitive control,perception of user intent, and enhanced interactive experiences.

Gaze tracking devices have proven to be extremely beneficial to impairedusers. In one case study presented (V. Pasian, F. Corno, I. Signorile,and L. Farinetti. The Impact of Gaze Controlled Technology on Quality ofLife. In Gaze Interaction and Applications of Eye Tracking: Advances inAssistive Technologies, chapter 6, pages 48-54. IGI Global, 2012.)sixteen amyotrophic lateral sclerosis (ALS) patients with severe motorimpairments (loss of mobility, unable to speak, etc.) were introduced toeye tracking devices during a 1-2 week period. The patients wereassessed by a psychologist during an initial meeting in order toevaluate their general quality of life. Eye tracking devices and propertraining, as well as access to a speech and language therapist and acomputer engineer, were provided for the duration of the study. Patientscompleted questionnaires related to their experiences with the equipmentseveral times during the study. Several patients reported a clearpositive impact on their quality of life during the study, resultingfrom the enhanced communication facilitated by the eye tracking devicesover other non-gaze based assistive devices.

While the utility of gaze interaction in a variety of applications hasbeen demonstrated, the availability of the technology has been alimiting factor in more widespread use. Due to the relatively highmonetary cost and proprietary nature associated with commercial eyetracking equipment and software, several low-cost solutions have beendeveloped using inexpensive on-the-shelf components. Many of thesedesigns have been made publicly available through the open sourcecommunity. The openEyes project (D. Li, J. Babcock, and D. J. Parkhurst.openEyes: a low-cost head-mounted eye-tracking solution. In Proceedingsof the 2006 symposium on Eye tracking research & applications—ETRA '06,page 95, New York, N.Y., USA, 2006. ACM Press.) presents a low-costhead-mounted eye tracker that uses a pair of inexpensive IEEE-1394cameras to capture images of both the eye and scene. This hardwaredevice, coupled with the open source Starburst algorithm, facilitatesestimation of the user PoG in the 2D scene image. A similar open sourceproject, the EyeWriter, provides detailed build instructions forcreating a head-mounted eye tracker from a modified Playstation Eye USBcamera. The project was designed to enable digital drawing by eye gazecontrol for artists with ALS while using the device with theaccompanying open source software. Interestingly, in J. San Agustin, H.Skovsgaard, J. P. Hansen, and D. W. Hansen. Low-cost gaze interaction:ready to deliver the promises. In Proceedings of the 27^(th)international conference extended abstracts on Human factors incomputing systems—CHI EA '09, page 4453, New York, N.Y., USA, 2009. ACMPress., the effectiveness of a low-cost eye tracker is shown to becomparable to that of commercial devices for target acquisition andeye-typing activities.

The head-mounted eye gaze systems mentioned above facilitate effectiveinteractive experiences with some limiting constraints. In general,these solutions are designed for interaction with fixed computerdisplays or 2D scene images. These types of systems provide a 2D PoG,which does not directly translate into the 3D world. An accurateestimate of the 3D user PoG can be especially useful in mobileapplications, human-robot interaction, and in designing intelligentassistive environments. Knowledge of the 3D PoG within an environmentcan be used to detect user attention and intention to interact, leadingto multimodal attentive systems able to adapt to the user state.

Some mobile 3D PoG tracking systems have been proposed in literature.For example, a head-mounted multi-camera system has been presented thatestimates the 3D PoG by computing the intersection of the optical axisof both eyes. This approach gives the 3D PoG relative to the user'sframe of reference, but does not provide a mapping of this point to theenvironment in which the user is present. A similar stereo cameraapproach is presented in K. Takemura, Y. Kohashi, T. Suenaga, J.Takamatsu, and T. Ogasawara. Estimating 3D point-of-regard andvisualizing gaze trajectories under natural head movements. InProceedings of the 2010 Symposium on Eye-Tracking Research &Applications—ETRA '10, volume 1, page 157, New York, N.Y., USA, 2010.ACM Press., which also includes a forward-facing scene camera formapping of the 3D PoG to scene coordinates. While multi-cameraapproaches such as these provide a 3D PoG, their use is limited byincreased uncertainty at increasing PoG depths. Another limiting factoris the scene camera, which is generally a standard 2D camera that doesnot provide any 3D information of the environment itself.

SUMMARY OF THE INVENTION

An point of gaze apparatus is presented. In one embodiment, theapparatus includes an eye tracking camera configured to track themovements of a user's eye. In some embodiments, a scene camera may beconfigured to create a three-dimensional image and a two-dimensionalimage in the direction of the user's gaze. In addition, in someembodiments, the point of gaze apparatus may include an image processingmodule that is configured to identify a point of gaze of the user andidentify an object located at the user's point of gaze. The point ofgaze apparatus may identify the object by using information from the eyetracking camera and the scene camera.

In some embodiments, the apparatus may include an illumination sourceconfigured to illuminate the user's eye. For example, the illuminationsource may be an infrared light emitting diode. In some embodiments, theeye tracking camera may include an infrared pass filter.

In some embodiments, the eye tracking camera and scene camera of thepoint of gaze apparatus may be mounted on a wearable headset.Furthermore, the scene camera may be an RGB-D camera.

In some embodiments, a point of gaze apparatus may include a means fortracking the movement of an eye. The means for tracking may be a USBcamera, for example. The point of gaze apparatus may include a means forimaging a scene. The means for imaging the scene may be an RGB-D camera,for example. Furthermore, the point of gaze apparatus may include ameans for using information gathered by the means for tracking andinformation from the means for imaging to identify an object seen by theeye. The means for imaging may be a general purpose computer programmedto perform the steps disclosed in the flow chart of FIG. 6. Furthermore,in some embodiments, the point of gaze apparatus may include a means formounting the means for tracking and means for imaging to a user's head.For example, the means for mounting may be a pair of goggles or glassesthat a user can wear.

A method is also presented for estimating a point of gaze. The method inthe disclosed embodiments substantially includes the steps necessary tocarry out the functions presented above with respect to the operation ofthe described apparatus and system. In one embodiment, the methodincludes tracking the movement of a user's eye with an eye trackingcamera. In addition, in one embodiment, the method may include obtaininga three-dimensional image and a two-dimensional image in the directionof the user's gaze. Furthermore, the method may include identifying anobject in a point of gaze of the user using the eye tracking camera,three-dimensional image, and two dimensional image.

In some embodiments, tracking the movement of the user's eye may includemeasuring a corneal reflection of the user's eye. In some embodiments,the method may include calibrating the eye tracking camera beforetracking the movement of the user's eye. Furthermore, according to thedisclosed methods, the user's point of gaze may be calculated using apupil tracking algorithm. In some embodiments, identifying the objectmay include identifying a euclidean cluster in the three-dimensionalimage closest to the user's point of gaze. Furthermore, the method mayinclude identifying a region of interest in the euclidean cluster andidentifying a shape of the object from points in the region of interest.For example, identification of the shape of the object may be performedusing the RANSAC algorithm.

In some embodiments, the method may include using a region of thetwo-dimensional image corresponding to the image cluster to identify theobject. In addition, the region of the two-dimensional image may becompared to a reference image. For example, the comparison may beperformed using the SURF method.

In some embodiments, identifying the object may include comparing ahistogram of a region of the two-dimensional image near the near thepoint of gaze to a reference histogram.

In some embodiments, the method may include calculating a plurality ofgeometric classification match scores between the object and a pluralityof reference objects. For example, the method may include calculating aplurality of keypoint match scores between the object and the pluralityof reference objects. In addition, the method may include calculating aplurality of histogram comparison scores between the object and theplurality of reference object. Also, the method may include identifyinga reference object based on the sum of geometric classification matchscore, keypoint match score, and histogram comparison score. In someembodiments, the sum is a weighted sum.

The term “coupled” is defined as connected, although not necessarilydirectly, and not necessarily mechanically.

The terms “a” and “an” are defined as one or more unless this disclosureexplicitly requires otherwise.

The term “substantially” and its variations are defined as being largelybut not necessarily wholly what is specified as understood by one ofordinary skill in the art, and in one non-limiting embodiment“substantially” refers to ranges within 10%, preferably within 5%, morepreferably within 1%, and most preferably within 0.5% of what isspecified.

The terms “comprise” (and any form of comprise, such as “comprises” and“comprising”), “have” (and any form of have, such as “has” and“having”), “include” (and any form of include, such as “includes” and“including”) and “contain” (and any form of contain, such as “contains”and “containing”) are open-ended linking verbs. As a result, a method ordevice that “comprises,” “has,” “includes” or “contains” one or moresteps or elements possesses those one or more steps or elements, but isnot limited to possessing only those one or more elements. Likewise, astep of a method or an element of a device that “comprises,” “has,”“includes” or “contains” one or more features possesses those one ormore features, but is not limited to possessing only those one or morefeatures. Furthermore, a device or structure that is configured in acertain way is configured in at least that way, but may also beconfigured in ways that are not listed.

Other features and associated advantages will become apparent withreference to the following detailed description of specific embodimentsin connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings form part of the present specification and areincluded to further demonstrate certain aspects of the presentinvention. The invention may be better understood by reference to one ormore of these drawings in combination with the detailed description ofspecific embodiments presented herein.

FIG. 1 is a headset hardware solution for a 3D Point of Gaze apparatus.

FIG. 2A is an image of an eye to illustrate calculations made todetermine a user's point of gaze.

FIG. 2B shows a user's gaze as he or she scans a table with objects.

FIGS. 3A-3F show the results of a disclosed method for identifying anobject at a user's point of gaze.

FIG. 4 shows an example of using SURF keypoint matches to identify anobject.

FIG. 5 shows an experimental setup for using a point of gaze apparatus.

FIG. 6 is a flow chart for a method of using a point of gaze apparatus.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Various features and advantageous details are explained more fully withreference to the nonlimiting embodiments that are illustrated in theaccompanying drawings and detailed in the following description.Descriptions of well-known starting materials, processing techniques,components, and equipment are omitted so as not to unnecessarily obscurethe invention in detail. It should be understood, however, that thedetailed description and the specific examples, while indicatingembodiments of the invention, are given by way of illustration only, andnot by way of limitation. Various substitutions, modifications,additions, and/or rearrangements within the spirit and/or scope of theunderlying inventive concept will become apparent to those skilled inthe art from this disclosure.

This application discloses a novel head-mounted system that providesadditional data modalities that are not present in previous solutions.We show that the effective integration of these modalities can provideknowledge of gaze interaction with environmental objects to aid thedevelopment of intelligent human spaces. The solution considers threekey data modalities for 3D PoG estimation and environment interaction inreal-time. First, an eye tracking camera is used to estimate the 2D PoG.Next, an RGB-D scene camera is used to acquire two additionalmodalities: A 3D representation of the environment structure and a colorimage in the direction of the user's gaze. Then, according to methodsdisclosed herein the 2D PoG is transformed to 3D coordinates, and showthat the objects are able to be identified using a combination ofcomputer vision techniques and 3D processing. The disclosed experimentalresults show that accurate classification results are achieved bycombining the multiple data modalities.

The solution presented in this disclosure is designed to provideinformation about the environment existing around the user, togetherwith the points or areas within the environment that the user interactswith visually. In order to realize these goals, a wearable headset wasdeveloped that provides a 3D scan of the area in front of the user, acolor image of this area, and an estimate of the user's visual PoG.These three data modalities are provided by an eye tracking camera,which observes the user's eye motions, and a forward facing RGB-Dcamera, providing the scene image and 3D representation. These twocomponents are mounted on rigid eyeglass frames such that their positionremains fixed relative to the user's head during movement. An example ofa complete headset hardware solution is shown in in FIG. 1.

Eye Tracking Camera

In one embodiment, the system eye tracking feature is accomplished usingan eye tracking camera 102 (such as an embedded USB camera module)equipped with an infrared pass filter 104. The user's eye is illuminatedwith a single infrared LED 106 to provide consistent image data invarious ambient lighting conditions. The LED 106 also produces a cornealrefection on the user's eye, which can be seen by the eye trackingcamera 102 and exploited to enhance tracking accuracy. The LED 106 maybe chosen according to particular guidelines to ensure that the devicecan be used safely for indefinite periods of time.

The eye tracking camera 102 is positioned such that the image frame iscentered in front of one of the user's eyes. The module can be easilymoved from the left or right side of the headset frame so that eithereye may be used (to take advantage of user preference or eye dominance),while fine adjustments to the camera position and orientation arepossible by manipulating the flexible mounting arm 108. In someembodiments, streaming video frames from the eye tracking cameral 102are provided with a resolution of 640×480 at a rate of 30 Hz, whichfacilitates accurate tracking of the pupil and corneal reflection usingcomputer vision techniques.

Scene RGB-D Camera

Information about the user's environment may be provided, for example,by a forward-facing RGB-D camera, such as the Asus XtionPRO Live. Thisdevice provides a 640×480 color image of the environment along with a640×480 depth range image at a rate of 30 Hz. The two images areobtained from individual imaging sensors and registered by the devicesuch that each color pixel value is assigned actual 3D coordinates inspace. This provides a complete scanning solution for the environment inthe form of 3D “point clouds”, which can be further processed insoftware.

Computational Approach

This section describes the computational approach that may be used forobject of interest identification and classification. In general, thefour steps of the process are to: 1) estimate the PoG using the eye andscene cameras, 2) assign a geometric classification based on the 3Dobject of interest structure, 3) perform visual classification usingSURF feature matching, color histograms, and, 4) fuse the multimodaldata for a final result.

Point of Gaze Estimation

An estimate of the user PoG may be computed using a pupil trackingalgorithm. For example, a modified version of the starburst algorithmpresented in D. Winfield and D. Parkhurst. Starburst: A hybrid algorithmfor video-based eye tracking combining feature-based and model-basedapproaches. 2005 IEEE Computer Society Conference on Computer Vision andPattern Recognition (CVPR'05)—Workshops, 3:79-79, 2005 may be used. Thisalgorithm creates a mapping between pupil positions and 2D scene imagecoordinates after a simple calibration routine is performed. During thepupil detection phase of the algorithm, an ellipse is fitted to thepupil such that the ellipse center provides an accurate estimate of thepupil center. The center of the infrared corneal reflection is detectedduring the next phase of the algorithm, which can then be used togetherwith the pupil center coordinates to create the calibration mapping.Another pupil tracking algorithm that may be used is described in RobustReal-Time Pupil Tracking in Highly Off-Axis Images, ETRA '12 Proceedingsof the Symposium on Eye Tracking Research and Applications, pp. 173-176,2012. FIG. 2A shows a graphical representation of a fitted pupil ellipse202 around a pupil 204 computed from a single eye tracking camera 102image frame.

The mapping from pupil coordinates 208 to 2D scene image coordinates maybe accomplished, in one embodiment, by a nine-point calibrationprocedure. During calibration, the user sequentially gazes upon ninedifferent points in the scene image. The pupil coordinates for eachcalibration point is saved, and the nine point mapping is used tointerpolate a 2D PoG from future eye tracking camera frames. The 3D PoGcan be obtained from the 2D points by looking up the 3D coordinates ofthe pixel in the point cloud data structure provided by the RGB-Dcamera. Exploitation of the RGB-D point cloud structure removes the needfor stereo eye tracking during 3D PoG estimation as used in othermethods.

FIG. 2B shows a user's gaze as he or she scans a table with objects.

Geometric Classification

Point cloud manipulation may be performed with the utilization of thePoint Cloud Library (PCL). PCL provides the methods necessary to extractinformation from point clouds, the contribution presented in thissection is the overall process for which the given methods are applied.

Instead of applying the model segmentation on the initial point cloud, aseries of operations may be performed on the point cloud to removepoints that are not of interest. A large portion of the point cloud iscomprised of these points, which include the points that correspond tothe floor, wall, or ceiling, and the points that lie outside the area ofinterest. One can assume these points are not of interests due to thefact that points of interest must provide interactivity and lie within areasonable distance to the user's PoG.

Planar models may be quicker to detect than more complex models, such ascylinders or spheres, so may be beneficial to remove large planes fromthe point cloud prior to detecting the models belonging to the moreinteractive geometries. Planes corresponding to tables, walls, theceiling or floor, will span a large portion of the point cloud. Due tothis it will not be necessary to perform the planar segmentation on thefull point cloud, and down sampling of the point cloud can be performed.This will provide a performance increase since the fidelity of the pointcloud is reduced, while allowing large models to maintain theirstructure within the point cloud. The removal of these large planes fromthe point cloud is useful in reducing the point cloud size, as thesewill not provide valuable interaction for the user.

Objects that are of interest are comprised of several points that arerelatively close together and are not disjoint. PCL provides a method todetect the euclidean clusters within a point cloud. These clusters arefound by linking points together that are within a defined distancethreshold, which further emphasizes the importance of removing largeplanes, since they will connect clusters that otherwise would bedisjoint. After the clusters are identified, the PoG is combined withthe point cloud to determine the cluster closest to the PoG. Thiscluster is extracted from the point cloud. The extracted clusterprovides a region of interest within the original point-cloud, and thefinal model segmentation is performed on the subset of points from theinitial point cloud that lie inside the area of the extracted clusterregion. When segmenting smaller objects, higher fidelity is needed withthe point cloud, which is why the region must be taken from the originalhigh-fidelity point cloud. When model segmentation is performed on thisfinal point cloud, cylinder and sphere models are detected. Modelparameter estimation may be done using the RANSAC algorithm. This modelparameter estimation is also done in similar fashion when estimating theplanar coefficients discussed previously. Final model classification isassigned based on the results of the segmentation over each of thespecified models. The currently-available geometric classificationsbelong to the set {cylinder, sphere, other}.

FIGS. 3A-3F show the results of manipulating a point cloud to identifyan object of interest. FIG. 3A shows an original point cloud of a scene.In this example there are three potential objects of interest, oatmeal304, a basketball 306 and raisins 308, all set on a table 310. In FIG.3B, the planes of the table 310 and walls have been removed, leavingonly the three potential object of interest. In FIG. 3C, the euclideanclustering is performed to identify the point cloud clusters around theobject of interest. In FIG. 3D, the euclidean cluster belonging to thebasketball is selected as being in the users PoG. In FIG. 3E,segmentation is performed to detect the shape of the model of interest(cylinder, sphere, or other). In FIG. 3F, a portion of the 2D imagecorresponding to the object of interest is cropped to include only theobject of interest.

Following the geometric classification, analysis is performed on thecropped RGB data to further classify the object. The input for thesemethods consists of the geometric classification and a cropped 2D RGBimage representing the final extracted point cloud. The cropped imagecomes from creating a bounding box relative to the 2D RGB image of theregion of interest containing the extracted cluster.

SURF Feature Matching

In order to reliably identify a query object by image comparison, thereneeds to be similarity between image features. Since it is unlikely thatthe object being identified will be in the same orientation and positionrelative to the reference image, it is important to calculate featuresthat are reproducible at different scales and viewing angles. Speeded UpRobust Features (SURF) is an efficient method to find such features,called keypoints, and calculate their descriptors, which containinformation about the grayscale pixel intensity distribution around thekeypoints.

The system maintains a knowledge base of SURF features and descriptorsfor all reference object images. For these images, the keypoints anddescriptors are precomputed and stored to avoid recalculation each timean object is to be identified. The feature/descriptor calculations forthe query object images, on the other hand, are necessarily performedon-the-fly as object identifications are requested.

In the SURF-based object identification we perform, the query objectimage keypoints are compared to those of each reference object image todetermine similarity. One method is to use a modified version of therobust feature matching approach described in R. Laganiere. OpenCV 2Computer Vision Application Programming Cookbook. Packt Publishing, June2011, to do so. A k-nearest-neighbors search is performed to match eachkeypoint descriptor in the query image with the two most similardescriptors in the reference image, and vice versa. These matches entera series of tests to narrow down the list of those that are accepted.First, if the two nearest-neighbor matches are too similar to reliablydetermine which is the better match, neither is used. Otherwise, thebest match is tentatively accepted. FIG. 4 shows several keypointmatches at this stage. Second, if a keypoint matching from the queryimage to the reference image is not also a match from the referenceimage to the query image, it is rejected. The surviving keypoint matchesare validated using the epipolar constraint so that any matched pointsnot lying on corresponding epipolar lines are rejected, and the numberof remaining matches is stored for each image in the knowledge base.

FIG. 4 shows an example of using SURF keypoint matches to identify anobject. The algorithm compares and matches keypoints 406 in a queryimage 402 to keypoints in a reference image 404.

Histogram Matching

Since multiple objects can produce similar features in SURFcalculations, it may be beneficial to incorporate color information intoobject identification. One may use color histograms to do so, since theyprovide a convenient way to represent the distribution of colors in animage and can easily and efficiently be compared. To minimize the effecton histogram matching of potential differences in brightness andcontrast between reference and query images, a normalized red-green (RG)color space may be used for the calculations.

The histograms we used contain eight bins in each dimension. So, for thenormalized RG color space, we used 2-dimensional 8×8 histograms for atotal of sixty-four bins. As with the SURF keypoints/descriptors, thehistograms for the reference object images are computed and stored inthe knowledge base for easy comparison later, while the histograms forthe test images are calculated at identification time. To identify aquery object by histogram matching, the similarity between the queryimage histogram and each reference image histogram is calculated usingnormalized cross-correlation to obtain a value in the range [−1, 1].

Data Fusion and Object Identification

To most reliably identify the object of interest, one may effectivelyincorporate the data from SURF feature matching, geometricclassification, and histogram comparison into a single score for eachobject in the reference set.

For example, after SURF keypoint match calculations, the number ofkeypoints matched from the query object image to each reference objectimage is stored as a raw score, n for that particular reference object.A final, normalized SURF score αε[0; 1] is calculated for each referenceobject i:

${\alpha_{i} = \frac{n_{i}}{m}},{{{for}\mspace{14mu} m} = {\max\limits_{i}\mspace{14mu} \left( n_{i} \right)}}$

Similarly, normalized cross-correlation values obtained from thehistogram comparisons are stored for each reference object image as araw histogram score, hε[−1; 1]. A final normalized histogram scoreβε[−1, 1] is calculated for each object i:

${\beta_{i} = \frac{h_{i}}{k}},{{{for}\mspace{14mu} k} = {\max\limits_{i}\mspace{14mu} \left( h_{i} \right)}}$

The third score we calculate is a simple geometric classification matchscore γ_(i) for each reference object image i. To determine γ_(i), thequery image's detected classification c is compared to the referenceclassification d_(i):

$\gamma_{i} = \left\{ \begin{matrix}1 & : & {c = d_{i}} \\0 & : & {c \neq d_{i}}\end{matrix} \right.$

A final score S_(i) is calculated for each object i as a linearcombination of the three scores. To do so, the SURF, histogram, andgeometric scores are assigned weights, w_(α), w_(β), w_(γ) ω ω, w, and wrespectively:

S _(i) =w _(α)α_(i) +w _(β)β_(i) +w _(γ)γ_(i)

The object O can now be identified as:

$O = {\underset{i}{argmax}\mspace{14mu} \left( S_{i} \right)}$

EXAMPLE

Referring to FIG. 5, to assess the ability of the system to identify theobject gazed upon by the user 502, we created an experiment to reproducea typical usage application in which the user is seated at a table anddesires assistance with an item 504 on the table. The user might, forexample, desire some water from a pitcher on the table, but be unable toreach for the object or request assistance through verbal means orgesturing.

To this end, we used the system software to create a knowledge base ofknown objects and placed an assortment of test items on the table toevaluate the system's ability to estimate the user's point of gaze, usethat information to isolate the object of interest, and performsuccessful identification.

Experimental Setup

During our experiment, a participant 502 sat in multiple positions infront of a table with an assortment of objects placed on top. They werefree to move their head, eyes, and body. We instructed the participantto focus their gaze on an object and notify us with a verbal cue whenthis was accomplished. On this cue, a trigger event for the system toidentify the object was issued. The PoG calibration was performed priorto system use, and the calibration result was checked for validity. Inthe experiment, the participant focused his gaze on each of the objectsfrom three different locations at distances of up to 2 meters.Calibration may be done by looking at known positions in a set order.For example, one can place a red dot on a wall and collect gaze pointsas the user moves his or her head slightly (so that the pupils movewhile following the dot). In addition a “calibration wand” may be usedto give the user a point on which to focus during the calibrationroutine.

Data was acquired using the headset described above, while computationswere performed in real-time on a Lenovo Ideapad Y560 laptop running theLinux operating system. The laptop was equipped with a 2.20 GHz Core i7processor with 4 GB DDR3 1333 memory.

The knowledge base used for image comparison and identificationconsisted of fifteen objects that varied in size from a baseball to amusical keyboard. Each object had two previously-collected trainingimages from different angles and distances, which had been obtainedusing the same headset and automatically cropped via the methoddescribed above.

Experimental Results

After running the experiments, the raw scores of the image comparisonswere processed to determine the optimal values for the three weightsdiscussed above. Once the score weights were adjusted, the results werecollected and analyzed. Table 1 shows the object identification accuracyfor the various classifiers in the system, both individually and incombination.

TABLE 1 Object identification results Classifier Accuracy SURF Matching0.711 Histogram Matching 0.622 SURF + Histograms 0.756 SURF +Histograms + Geometry 0.844

As can be seen from the results, the ability to identify the object of auser's gaze significantly improves as additional classifiers are added.Since SURF feature matching is a popularly used method of objectmatching, we use its accuracy as a baseline for our analysis. We see asignificant 18.7% increase in correct object identifications byincorporating color histogram and geometric classification data withSURF matching. These results clearly illustrate the benefit of fusingmultiple data modalities. The average execution times, in seconds, foreach step in the identification method are presented in Table 2.

TABLE 2 Table of average runtimes Classifier Execution time (s)Geometric Classification 0.329 SURF Matching 0.201 Histogram Matching0.001

The systems and methods disclosed herein illustrate impact of combiningPoG estimation techniques with low-cost 3D scanning devices such asRGB-D cameras. The data modalities provided by the headset can beanalyzed in such a way that user intent and visual attention can bedetected and utilized by other environment actors, such as caregivers orrobotic agents.

The results of the experiment show that the combination ofclassification methods using multiple data modalities increases overallaccuracy. Weighting the individual classification methods in the finaldata fusion step allows for a higher emphasis to be placed on differentmodalities at different times, which could facilitate dynamic adjustmentof weights based on external factors such as lighting conditions.

While the experimental portion of this work focused mainly on 3D objectrecognition, the 3D PoG estimation provided by the combination of eyetracking and RGB-D modalities is extremely useful by itself. The utilityof this approach warrants further investigation and comparison withexisting 3D PoG methods, such as stereo eye tracking. Given that theinclusion of the RGB-D scene camera removes the need for multiple eyetracking cameras, it follows that the area obstructed by optical devicesin the user's field of vision would be minimized. The trade-off betweenmultiple eye tracking cameras and a bulkier RGB-D scene camera willlikely improve significantly with time as the technology matures andminiaturizes.

The schematic flow chart diagrams that follow are generally set forth aslogical flow chart diagrams. As such, the depicted order and labeledsteps are indicative of one embodiment of the presented method. Othersteps and methods may be conceived that are equivalent in function,logic, or effect to one or more steps, or portions thereof, of theillustrated method. Additionally, the format and symbols employed areprovided to explain the logical steps of the method and are understoodnot to limit the scope of the method. Although various arrow types andline types may be employed in the flow chart diagrams, they areunderstood not to limit the scope of the corresponding method. Indeed,some arrows or other connectors may be used to indicate only the logicalflow of the method. For instance, an arrow may indicate a waiting ormonitoring period of unspecified duration between enumerated steps ofthe depicted method. Additionally, the order in which a particularmethod occurs may or may not strictly adhere to the order of thecorresponding steps shown.

FIG. 6 illustrates one embodiment of a method 600 for use with a mobile,low-cost headset for 3D point of gaze estimation. In one embodiment, themethod 600 includes the step 602 of tracking the movement of a user'seye with an eye tracking camera. As discussed above, the eye trackingcamera may be a USB camera mounted on a headset. At step 604, the methodincludes the step of obtaining a three-dimensional image and atwo-dimensional image of the user's field of view. The two images may beobtained using an RGB-D camera. At step 606 the method may include thestep of identifying an object of interest. The object of interest may bea euclidean cluster in the 3D point cloud or a cropped image in the 2Dimage. At step 608, the method may include the step of creating ageometric classification of the object of interest. For example, theobject of interest may be identified as a sphere or a cylinder. At step610 the method may include creating a histogram of the object ofinterest. The histogram may describe the colors exhibited by the object.At step 612, the method may include the step of creating a keypointmatch score for the object of interest. As described above, the keypointmatch score may be computed using the SURF algorithm. Finally, themethod may include the step of using the geometric classification,histogram, and keypoint match score to identify the object of interest.In some embodiments, the geometric classification, histogram, andkeypoint match score may be weighted to increase the accuracy of themethod in identifying the object.

All of the methods disclosed and claimed herein can be made and executedwithout undue experimentation in light of the present disclosure. Whilethe apparatus and methods of this invention have been described in termsof preferred embodiments, it will be apparent to those of skill in theart that variations may be applied to the methods and in the steps or inthe sequence of steps of the method described herein without departingfrom the concept, spirit and scope of the invention. For example, insome embodiments, a histogram may be particularly helpful (and thereforemore heavily weighted) if the objects of interest are color-coded. Inaddition, modifications may be made to the disclosed apparatus andcomponents may be eliminated or substituted for the components describedherein where the same or similar results would be achieved. All suchsimilar substitutes and modifications apparent to those skilled in theart are deemed to be within the spirit, scope, and concept of theinvention as defined by the appended claims.

1. A point of gaze apparatus, comprising: an eye tracking camera,configured to track the movements of a user's eye; a scene cameraconfigured to create a three-dimensional image and a two-dimensionalimage in the direction of the user's gaze; and an image processingmodule configured to identify a point of gaze of the user and identifyan object located at the user's point of gaze by using information fromthe eye tracking camera and the scene camera.
 2. The apparatus of claim1, further comprising an illumination source configured to illuminatethe user's eye.
 3. The apparatus of claim 2, where the illuminationsource is an infrared light emitting diode.
 4. The apparatus of claim 3,where the eye tracking camera further comprises an infrared pass filter.5. The apparatus of claim 1, where the eye tracking camera and scenecamera are mounted on a wearable headset.
 6. The apparatus of claim 1,where the scene camera is an RGB-D camera.
 7. A point of gaze apparatus,the apparatus comprising: a means for tracking the movement of an eye; ameans for imaging a scene; and a means for using information gathered bythe means for tracking and information from the means for imaging toidentify an object seen by the eye.
 8. The apparatus of claim 7, furthercomprising a means for mounting the means for tracking and means forimaging to a user's head.
 9. A method for estimating a point of gaze,the method comprising: tracking the movement of a user's eye with an eyetracking camera; obtaining a three-dimensional image and atwo-dimensional image in the direction of the user's gaze; andidentifying an object in a point of gaze of the user using the eyetracking camera, three-dimensional image, and two dimensional image. 10.The method of claim 9, where tracking the movement of the user's eyecomprises measuring a corneal reflection of the user's eye.
 11. Themethod of claim 9, further comprising calibrating the eye trackingcamera before tracking the movement of the user's eye.
 12. The method ofclaim 9, where the user's point of gaze is calculated using a pupiltracking algorithm.
 13. The method of claim 9, where identifying theobject comprises: identifying a euclidean cluster in thethree-dimensional image closest to the user's point of gaze; identifyinga region of interest in the euclidean cluster; and identifying a shapeof the object from points in the region of interest.
 14. The method ofclaim 13, where the identification of the shape of the object isperformed using the RANSAC algorithm.
 15. The method of claim 13,further comprising using a region of the two-dimensional imagecorresponding to the image cluster to identify the object.
 16. Themethod of claim 15, where the region of the two-dimensional image iscompared to a reference image.
 17. The method of claim 16, where thecomparison is performed using the SURF method.
 18. The method of claim9, where identifying the object further comprises comparing a histogramof a region of the two-dimensional image near the near the point of gazeto a reference histogram.
 19. The method of claim 9, where identifyingthe object comprises: calculating a plurality of geometricclassification match scores between the object and a plurality ofreference objects; calculating a plurality of keypoint match scoresbetween the object and the plurality of reference objects; calculating aplurality of histogram comparison scores between the object and theplurality of reference object; and identifying a reference object basedon the sum of geometric classification match score, keypoint matchscore, and histogram comparison score.
 20. The method of claim 19, wherethe sum is a weighted sum.